The accuracy of the MMSE in detecting cognitive impairment when administered by general practitioners: A prospective observational study

<p>Abstract</p> <p>Background</p> <p>The Mini-Mental State Examination (MMSE) has contributed to detecting cognitive impairment, yet few studies have evaluated its accuracy when used by general practitioners (GP) in an actual public-health setting.</p> <p>Objectives</p> <p>We evaluated the accuracy of MMSE scores obtained by GPs by comparing them to scores obtained by Alzheimer's Evaluation Units (UVA).</p> <p>Methods</p> <p>The study was observational in design and involved 59 voluntary GPs who, after having undergone training, administered the MMSE to patients with symptoms of cognitive disturbances. Individuals who scored ≤ 24 (adjusted by age and educational level) were referred to Alzheimer's Evaluation Units (UVA) for diagnosis (including the MMSE). UVAs were unblinded to the MMSE score of the GP. To measure interrater agreement, the weighted Kappa statistic was calculated. To evaluate factors associated with the magnitude of the difference between paired scores, a linear regression model was applied. To quantify the accuracy in discriminating no cognitive impairment from any cognitive impairment and from Alzheimer's disease (AD), the ROC curves (AUC) were calculated.</p> <p>Results</p> <p>For the 317 patients, the mean score obtained by GPs was significantly lower (15.8 vs. 17.4 for the UVAs; p < 0.01). However, overall concordance was good (Kappa = 0.86). Only the diagnosis made by the UVA was associated with the difference between paired scores: the adjusted mean difference was 3.1 for no cognitive impairment and 3.8 for mild cognitive impairment. The AUC of the scores for GPs was 0.80 (95%CI: 0.75–0.86) for discriminating between no impairment and any impairment and 0.89 (95%CI: 0.84–0.94) for distinguishing patients with AD, though the UVA scores discriminated better.</p> <p>Conclusion</p> <p>In a public-health setting involving patients with symptoms of cognitive disturbances, the MMSE used by the GPs was sufficiently accurate to detect patients with cognitive impairment, particularly those with dementia.</p

Use of health and social care services in a cohort of Italian dementia patients

The aim of this study, conducted in the Region of Lazio, Italy, in 2008-2010, was to describe the use, over a one-year period, of health and social care services in a cohort of 712 patients with a diagnosis of dementia. These patients had never previously used such services. We evaluated the association between the patients’ sociodemographic and clinical characteristics and their use of services. Sociodemographic and clinical data were collected at baseline using validated instruments, while the use of services was investigated at the end of the one-year follow-up through a structured (questionnaire-based) interview with the caregiver. We found that 11.9% of patients used health or social care services. The most frequent diagnoses were: Alzheimer’s disease (72.1%), mixed dementia (20.5%), and vascular dementia (9.7%). A higher probability of use of services was observed in patients with: more than five years of schooling (OR=1.79; 95%CI:1.08-2.96); one or more comorbidity (OR=4.87; 95%CI:2.05-11.57); severe (OR=4.78; 95%CI:1.75-13.06) or moderate dementia (OR=2.08; 95%CI:0.98-4.40). The low health and social care service use among dementia patients in this study could be explained by a lack of availability of services. Public health authorities should plan adequate networks of services, considering both patients and caregivers’ needs

Identification of dementia and MCI cases in health information systems: An Italian validation study

Abstract Introduction The identification of dementia cases through routinely collected health data represents an easily accessible and inexpensive method to estimate the prevalence of dementia. In Italy, a project aimed at the validation of an algorithm was conducted. Methods The project included cases (patients with dementia or mild cognitive impairment [MCI]) recruited in centers for cognitive disorders and dementias and controls recruited in outpatient units of geriatrics and neurology. The algorithm based on pharmaceutical prescriptions, hospital discharge records, residential long‐term care records, and information on exemption from health‐care co‐payment, was applied to the validation population. Results The main analysis was conducted on 1110 cases and 1114 controls. The sensitivity, specificity, and positive and negative predictive values in discerning cases of dementia were 74.5%, 96.0%, 94.9%, and 79.1%, respectively, whereas in detecting cases of MCI these values were 29.7%, 97.5%, 92.2%, and 58.1%, respectively. The variables associated with misclassification of cases were also identified. Discussion This study provided a validated algorithm, based on administrative data, which can be used to identify cases with dementia and, with lower sensitivity, also early onset dementia but not cases with MCI